The present disclosure relates generally to apparatuses, systems, and methods for integrated real time low-cost automatic load disaggregation, remote monitoring, and control.
At the consumer level, the conventional practice of power metering is typically manually recorded. Furthermore, the consumer only gets an electric bill with no details about which load consumes which parts of the aggregate power consumption. This is true in homes, apartment complexes, commercial, and industrial buildings. Measuring the power consumption of individual major loads may be used as a diagnosis tool when the appliance/device starts to malfunction. Finally knowing the energy consumption of individual loads such a Heating, Ventilation, and Air Conditioning (HVAC) unit may alter the customer behavior to automatically reduce energy consumption by performing remote local control of such loads using appropriate automatic intelligent control schemes. To locally control a particular load, such load needs to be equipped with an affordable remote control infrastructure.
Smart meters have been in existence since the early 2000's as an attempt to automate the process, among other applications. Beside their high cost, they suffer from the specificity of the protocols used to acquire, transmit, and store data in real time. The lack of universality and high cost of smart meters make them less attractive to consumers. Furthermore, existing smart meters do not integrate the functions of monitoring and remote intelligent control in order to perform adaptive local load control to show the potential impact load management. Some of the existing smart meters are limited to switching the local load ON/OFF based on preconfigured off line thresholds.
At the utility level, in order to reduce the peak load, utility companies may provide economic incentive to the consumer to have the ability to remotely control individually-targeted residential, commercial, and industrial loads in real time. To make it attractive to utility companies, the upfront infrastructure cost should be minimal. Automatic intelligent adaptive load control strategies are needed to optimize the local load consumption and help maintain the stability of the grid by prioritizing the loads to be turned OFF during peak load or injecting local energy, such as the one from an electric car battery or renewable energy sources, into the grid.
In either customer or utility levels, for the purpose of load control/management, the concept of load disaggregation emerged to identify the consumption of targeted loads, such as HVAC. For load disaggregation, basically two approaches have been considered: Hardware- and software-based approaches. In hardware-based solutions, the simplest idea is to use a power meter for each load. This approach provides accurate measurement of the individual load consumption. However, it is prohibitively expensive, as it requires a separate meter for each appliance.
In an attempt to reduce the hardware used for load sub-metering, the concept of wireless sensors connected to a hub to measure individual load consumption was used. Even though this approach does not require a separate meter for individual loads, the use of wireless sensors is expensive and may pose interference problems as the number of nearby wireless sensors increases. Furthermore, special communication protocols have to be used to acquire the load data. Similar problems exist with wired networks, as both have added overhead in terms of communications and an increased number of processing units. Finally, this approach does not integrate the load disaggregation and load control functions.
In software-based sub-metering, the main idea is to use the aggregate load consumption and estimate the power consumption of targeted appliances/loads. This approach uses the aggregate load to extract individual loads. It uses advanced signal processing and matching techniques. This approach is inexpensive. However, it is inaccurate.
The inclusion of renewable energy generation is becoming more common as we move forward. Not only are large, special-purpose generation farms becoming more prevalent, but so also are smaller installations commonly seen both commercially and residentially. Small-scale installations are typically solar-based, but wind sources are sometimes used as well. One of the important aspects relating to renewable energy is the desire to use all that is available. Known as Maximum Power Point Tracking (MPPT), this goal has been extensively studied and is the primary focus of many patents and patent application publications, with U.S. Pat. Nos. 6,433,522, 7,371,963, 4,404,472, 4,649,334, 6,281,485, 6,281,485, 5,327,071, 4,525,633, 7,193,872, 7,158,395, 6,255,804, 5,869,956, 6,046,919, US20100236612, and U.S. Pat. No. 7,042,195 being just a few of the many. While using MPPT strategies allow maximizing the utility of renewable resources, there are times when the sources must operate below their maximum to meet grid requirements. Further, much of the work involved involves the use of DC-to-DC converters to enable MPPT ability. Similar to what is covered under U.S. Pat. No. 7,371,963, the DC-to-DC converters can also allow many renewable sources running at different voltages to share a single set of common busses.
Like renewable energy, electric vehicles have been increasing in popularity. Each electric vehicle must have large battery banks to allow it to travel even relatively short distances. While many only focus on charging their vehicles, this large amount of stored energy has great potential for helping the grid. This is again a popular research topic that also has a large number of patents and patent application publications describing it, including US2011/0202418, US2013/0179061, US2007/0282495, U.S. Pat. Nos. 7,844,370, 7,747,739, and US2012/0109798. These examples cover different ways to better utilize electric vehicles by allowing the vehicles to charge when it is best for the grid and to provide power back to the grid when it is best, such as during peak hours. Some of the research also delves into working with aggregating the vehicles for easier control, as is covered in U.S. Pat. Nos. 7,844,370, 7,747,739. The system in US2010/0274656 works with managing the charging of multiple electric vehicles, working to verify that a vehicle is legitimate and that the energy taken is properly paid for.